Damien Teney

18

Papers

39

Total Citations

Papers (18)

Neural Redshift: Random Networks are not Random Functions

Bayesian Low-Rank Learning (Bella): A Practical Approach to Bayesian Neural Networks

Synergy and Diversity in CLIP: Enhancing Performance Through Adaptive Backbone Ensembling

Do We Always Need the Simplicity Bias? Looking for Optimal Inductive Biases in the Wild

Mysteries of the Deep: Role of Intermediate Representations in Out of Distribution Detection

NeurIPS 2025arXiv

Selective Mixup Helps with Distribution Shifts, But Not (Only) because of Mixup

Counterfactual Vision and Language Learning

Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions With Superior OOD Generalization

Active Learning by Feature Mixing

Image Retrieval on Real-Life Images With Pre-Trained Vision-and-Language Models

Unshuffling Data for Improved Generalization in Visual Question Answering

Beyond Question-Based Biases: Assessing Multimodal Shortcut Learning in Visual Question Answering

Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision

Predicting Is Not Understanding: Recognizing and Addressing Underspecification in Machine Learning

On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law

NeurIPS 2020arXiv

Counterfactual Vision-and-Language Navigation: Unravelling the Unseen

SelecMix: Debiased Learning by Contradicting-pair Sampling

NeurIPS 2022arXiv

ID and OOD Performance Are Sometimes Inversely Correlated on Real-world Datasets

NeurIPS 2023arXiv